Planning Your Own Debt

We model the Danish market for mortgage backed securities with a two-factor interest rate model and use a stochastic programming approach to analyse how an individual home-owner should initially compose and subsequently readjust his mortgage in an optimal way. Results show that the 'rules of thumb' used by financial institutions are reasonable, although best suited for more aggressive mortgagors, for whom the delivery option is of some value. More risk-averse investors should also re-adjust frequently, but use more diversified portfolios. Results are insensitive to whether a one- or two-factor model is used, provided the former is suitably calibrated.     

EPP协议在车载地震仪中的设计与应用

针对并行口设备开发的趋势及特点 ,详细介绍了 EPP协议的产生、特点及时序 ;并结合科研项目“ES2 42 0地震仪改造”提出了用 EPP协议仿 Intel 80 86系统接口的详细设计方案 ,该接口由读写逻辑生成电路、地址锁存器及译码器、仿 Intel 80 86总线及一些逻辑延时电路组成。并给出了电路框图及 Windows3 .x/95/98编程。经扩展后总线的双向通信速度为 850 KB/s与 ISA总线的速度相当。     

Ishikawa迭代的非线性算子不动点逼近

本文在一致光滑的 Banach 空间,运用 Ishikawa 迭代,给出了方程解的逼近,进而将 C.E.Chidume 所研究结果从 Lipschitz 条件降至一致连续。关键词:巴拿赫空间;非线性算子;Ishikawa 迭代;强增生算子;强伪压缩算子;一致连续     

Optimizing the credit term decisions in supply chain finance

We study a credit term determination problem in the context of a supplier-buyer supply chain. The supplier's credit term decision is simultaneously made with its production and inventory decisions, and most importantly, it is impacted by the buyer's order quantity. We present a new game-theoretic framework to model this problem, which captures the interaction between the supplier's credit term decision and the buyer's order decision in a multi-period setting. An exact method based on nonlinear programming is implemented to obtain the optimal solutions. We apply our methodologies on a real world case. The computational results show that our approach significantly outperforms the heuristics with fixed credit terms, and either a short or a long credit term can be sub-optimal for the supplier in profitability. Our work offers the first data-driven model and solution approach that assists purchasing and supply managers to make optimal dynamic credit term decision in conjunction with production, ordering and inventory decisions in a game-theoretic setting.     

数控车床加工实训中的故障分析

本文通过分析学生在数控车床实训时易产生的故障和操作失误,阐述了故障发生的原因、避免或排除的对策,以切实提高学生的数控加工能力。     

A decision method for supplier selection in multi-service outsourcing

Although supplier selection in multi-service outsourcing is a very important decision problem, research concerning this issue is still relatively scarce. This paper proposes a decision method for selecting a pool of suppliers for the provision of different service process/product elements. It pioneers the use of collaborative utility between partner firms for supplier selection. A multi-objective model is built to select desired suppliers. This model is proved to be NP-hard, so we develop a multi-objective algorithm based on Tabu search for solving it. We then use an example to show the applicability of the proposed model and algorithm. Extensive computational experiments are also conducted to further test the performance of the proposed algorithm.     

A general endogenous grid method for multi-dimensional models with non-convexities and constraints

The endogenous grid method (EGM) significantly speeds up the solution of stochastic dynamic programming problems by simplifying or completely eliminating root-finding. We propose a general and parsimonious EGM extended to handle (1) multiple continuous states and choices, (2) multiple occasionally binding constraints, and (3) non-convexities such as discrete choices. Our method enjoys the speed gains of the original one-dimensional EGM, while avoiding expensive interpolation on multi-dimensional irregular endogenous grids. We explicitly define a broad class of models for which our solution method is applicable, and illustrate its speed and accuracy using a consumption–saving model with both liquid assets and illiquid pension assets and a discrete retirement choice.     

Monitoring as a partially observable decision problem

Monitoring is an important and costly activity in resource management problems such as containing invasive species, protecting endangered species, preventing soil erosion, and regulating contracts for environmental services. Recent studies have viewed optimal monitoring as a Partially Observable Markov Decision Process (POMDP), which provides a framework for sequential decision making under stochastic resource dynamics and uncertainty about the resource state. We present an overview of the POMDP framework and its applications to resource monitoring. We discuss the concept of the information content provided by monitoring systems and illustrate how information content affects optimal monitoring strategies. Finally, we demonstrate how the timing of monitoring in relation to resource treatment and transition can have substantial effects on optimal monitoring strategies.     

Sustainable hub location under mixed uncertainty

This paper addresses a novel sustainable hub location problem (SHLP) in which two new environmental-based cost functions accounting for air and noise pollution of vehicles are incorporated. To cope with uncertain data incorporated in the model, a mixed possibilistic–stochastic programming approach is proposed to construct the crisp counterpart. A simulated annealing (SA) and an imperialist competitive algorithm (ICA) with a new solution representation are developed to solve real-sized instances whose performances are compared with a proposed lower bound. Finally, some computational experiments are provided to demonstrate the effectiveness of the proposed model and solution approaches.     

Maximizing benefits in project selection: a hybrid approach

Charitable foundations and government programmes should endeavour to allocate their limited resources to best serve their constituents. Yet, mathematical programming techniques are rarely used despite overwhelming evidence of their superiority in selecting projects that yield higher levels of total benefits. We present a novel ‘hybrid selection model’ that combines binary linear programming and heuristic rank-based models applied to two case studies. The first case focuses on providing services to women and shows a hybrid model would have selected the top three ‘signature’ projects and maintained an above-average overall project benefit while securing a 180% improvement in the number of projects funded, a 66% improvement in the number of women served and a 132% improvement in the total benefit achieved. In the second case, we apply the hybrid approach to data from the US government’s largest forest preservation programme and demonstrate that the hybrid approach could allow the programme to select up to 11 top-scoring projects while still achieving a 97% gain in the total overall benefit compared to their traditional method. These case studies show that the hybrid approach has the potential to be applied in a variety of settings and improve how foundations and programmes achieve their goals.